# Face Detection in Repeated Settings

## TL;DR

This paper introduces a correlation-based face detection algorithm optimized for controlled location settings, outperforming Viola-Jones in accuracy and speed in scenarios with fixed backgrounds but variable pose, lighting, and scale.

## Contribution

The paper presents a novel, fast, and easy-to-train face detection method tailored for environments with controlled location but uncontrolled pose, lighting, and scale.

## Key findings

- Outperforms Viola-Jones in accuracy
- Faster detection times
- Easy and quick to train

## Abstract

Face detection is an important first step before face verification and recognition. In unconstrained settings it is still an open challenge because of the variation in pose, lighting, scale, background and location. However, for the purposes of verification we can have a control on background and location. Images are primarily captured in places such as the entrance to a sensitive building, in front of a door or some location where the background does not change. We present a correlation based face detection algorithm to detect faces in such settings, where we control the location, and leave lighting, pose, and scale uncontrolled. In these scenarios the results indicate that our algorithm is easy and fast to train, outperforms Viola and Jones face detection accuracy and is faster to test.